class_docstr: Creates an actor network.
code: def __init__(
      self,
      input_tensor_spec,
      output_tensor_spec,
      unbounded_actions=False,
      name=None,
  ):
      super(DummyActorNetwork, self).__init__(
          input_tensor_spec=input_tensor_spec, state_spec=(), name=name
      )
  
      self._output_tensor_spec = output_tensor_spec
      self._unbounded_actions = unbounded_actions
      activation = (
          None if unbounded_actions else tf.keras.activations.tanh
      )
  
      self._single_action_spec = tf.nest.flatten(output_tensor_spec)[0]
      self._layer = tf.keras.layers.Dense(
          self._single_action_spec.shape.num_elements(),
          activation=activation,
          kernel_initializer=tf.constant_initializer([2, 1]),
          bias_initializer=tf.constant_initializer([5]),
          name="action",
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_agents.agents.ddpg import ddpg_agent
  from tf_agents.networks import network
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
  from tf_agents.utils import test_utils
label_desc: Use image node_id 1 to create a new DummyActorNetwork object from inherited base classes: network with example: obj = DummyActorNetwork(input_tensor_spec, output_tensor_spec, unbounded_actions, name)